亚洲男人的天堂2018av,欧美草比,久久久久久免费视频精选,国色天香在线看免费,久久久久亚洲av成人片仓井空

We consider the question of adaptive data analysis within the framework of convex optimization. We ask how many samples are needed in order to compute $\epsilon$-accurate estimates of $O(1/\epsilon^2)$ gradients queried by gradient descent, and we provide two intermediate answers to this question. First, we show that for a general analyst (not necessarily gradient descent) $\Omega(1/\epsilon^3)$ samples are required. This rules out the possibility of a foolproof mechanism. Our construction builds upon a new lower bound (that may be of interest of its own right) for an analyst that may ask several non adaptive questions in a batch of fixed and known $T$ rounds of adaptivity and requires a fraction of true discoveries. We show that for such an analyst $\Omega (\sqrt{T}/\epsilon^2)$ samples are necessary. Second, we show that, under certain assumptions on the oracle, in an interaction with gradient descent $\tilde \Omega(1/\epsilon^{2.5})$ samples are necessary. Our assumptions are that the oracle has only \emph{first order access} and is \emph{post-hoc generalizing}. First order access means that it can only compute the gradients of the sampled function at points queried by the algorithm. Our assumption of \emph{post-hoc generalization} follows from existing lower bounds for statistical queries. More generally then, we provide a generic reduction from the standard setting of statistical queries to the problem of estimating gradients queried by gradient descent. These results are in contrast with classical bounds that show that with $O(1/\epsilon^2)$ samples one can optimize the population risk to accuracy of $O(\epsilon)$ but, as it turns out, with spurious gradients.

The emerging public awareness and government regulations of data privacy motivate new paradigms of collecting and analyzing data that are transparent and acceptable to data owners. We present a new concept of privacy and corresponding data formats, mechanisms, and theories for privatizing data during data collection. The privacy, named Interval Privacy, enforces the raw data conditional distribution on the privatized data to be the same as its unconditional distribution over a nontrivial support set. Correspondingly, the proposed privacy mechanism will record each data value as a random interval (or, more generally, a range) containing it. The proposed interval privacy mechanisms can be easily deployed through survey-based data collection interfaces, e.g., by asking a respondent whether its data value is within a randomly generated range. Another unique feature of interval mechanisms is that they obfuscate the truth but do not perturb it. Using narrowed range to convey information is complementary to the popular paradigm of perturbing data. Also, the interval mechanisms can generate progressively refined information at the discretion of individuals, naturally leading to privacy-adaptive data collection. We develop different aspects of theory such as composition, robustness, distribution estimation, and regression learning from interval-valued data. Interval privacy provides a new perspective of human-centric data privacy where individuals have a perceptible, transparent, and simple way of sharing sensitive data.

The Coronavirus disease 2019 (COVID-19) outbreak quickly spread around the world, resulting in over 240 million infections and 4 million deaths by Oct 2021. While the virus is spreading from person to person silently, fear has also been spreading around the globe. The COVID-19 information from the Australian Government is convincing but not timely or detailed, and there is much information on social networks with both facts and rumors. As software engineers, we have spontaneously and rapidly constructed a COVID-19 information dashboard aggregating reliable information semi-automatically checked from different sources for providing one-stop information sharing site about the latest status in Australia. Inspired by the John Hopkins University COVID-19 Map, our dashboard contains the case statistics, case distribution, government policy, latest news, with interactive visualization. In this paper, we present a participant's in-person observations in which the authors acted as founders of //covid-19-au.com/ serving more than 830K users with 14M page views since March 2020. According to our first-hand experience, we summarize 9 lessons for developers, researchers and instructors. These lessons may inspire the development, research and teaching in software engineer aspects for coping with similar public crises in the future.

As machine learning algorithms become increasingly integrated in crucial decision-making scenarios, such as healthcare, recruitment, and risk assessment, there have been increasing concerns about the privacy and fairness of such systems. Federated learning has been viewed as a promising solution for collaboratively training of machine learning models among multiple parties while maintaining the privacy of their local data. However, federated learning also poses new challenges in mitigating the potential bias against certain populations (e.g., demographic groups), as this typically requires centralized access to the sensitive information (e.g., race, gender) of each data point. Motivated by the importance and challenges of group fairness in federated learning, in this work, we propose FairFed, a novel algorithm to enhance group fairness via a fairness-aware aggregation method, which aims to provide fair model performance across different sensitive groups (e.g., racial, gender groups) while maintaining high utility. This formulation can further provide more flexibility in the customized local debiasing strategies for each client. We build our FairFed algorithm around the secure aggregation protocol of federated learning. When running federated training on widely investigated fairness datasets, we demonstrate that our proposed method outperforms the state-of-the-art fair federated learning frameworks under a high heterogeneous sensitive attribute distribution. We also investigate the performance of FairFed on naturally distributed real-life data collected from different geographical locations or departments within an organization.

We present our case study that aims to help professional assessors make decisions in human assessment, in which they conduct interviews with assessees and evaluate their suitability for certain job roles. Our workshop with two industrial assessors revealed that a computational system that can extract nonverbal cues of assesses from interview videos would be beneficial to assessors in terms of supporting their decision making. In response, we developed such a system based on an unsupervised anomaly detection algorithm using multimodal behavioral features such as facial keypoints, pose, head pose, and gaze. Moreover, we enabled the system to output how much each feature contributed to the outlierness of the detected cues with the purpose of enhancing its interpretability. We then conducted a preliminary study to examine the validity of the system's output by using 20 actual assessment interview videos and involving the two assessors. The results suggested the advantages of using unsupervised anomaly detection in an interpretable manner by illustrating the informativeness of its outputs for assessors. Our approach, which builds on top of the idea of separation of observation and interpretation in human-AI teaming, will facilitate human decision making in highly contextual domains, such as human assessment, while keeping their trust in the system.

Privacy protection is an essential issue in personalized news recommendation, and federated learning can potentially mitigate the privacy concern by training personalized news recommendation models over decentralized user data.For a theoretical privacy guarantee, differential privacy is necessary. However, applying differential privacy to federated recommendation training and serving conventionally suffers from the unsatisfactory trade-off between privacy and utility due to the high-dimensional characteristics of model gradients and hidden representations. In addition, there is no formal privacy guarantee for both training and serving in federated recommendation. In this paper, we propose a unified federated news recommendation method for effective and privacy-preserving model training and online serving with differential privacy guarantees. We first clarify the notion of differential privacy over users' behavior data for both model training and online serving in the federated recommendation scenario. Next, we propose a privacy-preserving online serving mechanism under this definition with differentially private user interest decomposition. More specifically, it decomposes the high-dimensional and privacy-sensitive user embedding into a combination of public basic vectors and adds noise to the combination coefficients. In this way, it can avoid the dimension curse and improve the utility by reducing the required noise intensity for differential privacy. Besides, we design a federated recommendation model training method with differential privacy, which can avoid the dimension-dependent noise for large models via label permutation and differentially private attention modules. Experiments on real-world news recommendation datasets validate the effectiveness of our method in achieving a good trade-off between privacy protection and utility for federated news recommendations.

Machine learning plays a role in many deployed decision systems, often in ways that are difficult or impossible to understand by human stakeholders. Explaining, in a human-understandable way, the relationship between the input and output of machine learning models is essential to the development of trustworthy machine-learning-based systems. A burgeoning body of research seeks to define the goals and methods of explainability in machine learning. In this paper, we seek to review and categorize research on counterfactual explanations, a specific class of explanation that provides a link between what could have happened had input to a model been changed in a particular way. Modern approaches to counterfactual explainability in machine learning draw connections to the established legal doctrine in many countries, making them appealing to fielded systems in high-impact areas such as finance and healthcare. Thus, we design a rubric with desirable properties of counterfactual explanation algorithms and comprehensively evaluate all currently-proposed algorithms against that rubric. Our rubric provides easy comparison and comprehension of the advantages and disadvantages of different approaches and serves as an introduction to major research themes in this field. We also identify gaps and discuss promising research directions in the space of counterfactual explainability.

Interest in the field of Explainable Artificial Intelligence has been growing for decades and has accelerated recently. As Artificial Intelligence models have become more complex, and often more opaque, with the incorporation of complex machine learning techniques, explainability has become more critical. Recently, researchers have been investigating and tackling explainability with a user-centric focus, looking for explanations to consider trustworthiness, comprehensibility, explicit provenance, and context-awareness. In this chapter, we leverage our survey of explanation literature in Artificial Intelligence and closely related fields and use these past efforts to generate a set of explanation types that we feel reflect the expanded needs of explanation for today's artificial intelligence applications. We define each type and provide an example question that would motivate the need for this style of explanation. We believe this set of explanation types will help future system designers in their generation and prioritization of requirements and further help generate explanations that are better aligned to users' and situational needs.

State-of-the-art recommendation algorithms -- especially the collaborative filtering (CF) based approaches with shallow or deep models -- usually work with various unstructured information sources for recommendation, such as textual reviews, visual images, and various implicit or explicit feedbacks. Though structured knowledge bases were considered in content-based approaches, they have been largely neglected recently due to the availability of vast amount of data, and the learning power of many complex models. However, structured knowledge bases exhibit unique advantages in personalized recommendation systems. When the explicit knowledge about users and items is considered for recommendation, the system could provide highly customized recommendations based on users' historical behaviors. A great challenge for using knowledge bases for recommendation is how to integrated large-scale structured and unstructured data, while taking advantage of collaborative filtering for highly accurate performance. Recent achievements on knowledge base embedding sheds light on this problem, which makes it possible to learn user and item representations while preserving the structure of their relationship with external knowledge. In this work, we propose to reason over knowledge base embeddings for personalized recommendation. Specifically, we propose a knowledge base representation learning approach to embed heterogeneous entities for recommendation. Experimental results on real-world dataset verified the superior performance of our approach compared with state-of-the-art baselines.